This fails in solve
In [1]: from sympy.stats import *
In [2]: mu = Symbol('mu', real=True, bounded=True)
In [3]: sigma = Symbol('sigma', positive=True)
In [4]: X = Normal('X', mu, sigma)
In [5]: simplify(2 * P(X - mu < 10))
Out[5]:
⎛ ___⎞
⎜5⋅╲╱ 2 ⎟
erf⎜───────⎟ + 1
⎝ σ ⎠
In [6]: solve(_ - 0.20, sigma)
NotImplementedError:
No algorithms are implemented to solve equation -_Dummy_44 +
erf(5*sqrt(2)/sigma)
Note that historically in statistics each distribution knew how to
calculate these things on their own. It was hard coded in. In stats we
express the problem with statistical operations and use SymPy to generate
the right equations to solve in each case. This is usually far more
powerful and extensible but, in this particular case, performs badly. The
ideal path to solution here is to teach solve how to deal with error
functions.
Does anyone know how hard this is?
On Sun, Oct 6, 2013 at 8:56 PM, Buck Shlegeris <buck.shlege...@anu.edu.au>wrote:
> I have the following code, using the statistics module. It answers
> questions like "If there's a 20% chance that my random variable is within
> 10 of the mean, what is the standard deviation?" with the call
> getStdDev(10,20).
>
> def getStdDev(distance,confidence):
> x = Normal(0,1)
> confidenceDistance = x.confidence(confidence)[1]
> stddev = confidenceDistance * distance
> return stddev
>
> The statistics module lets me do this with the confidence method. However,
> the statistics module is deprecated.
>
> Is there an equivalent in the stats module?
>
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